Advanced Genomic Data Mining

  Data mining is vital to bioinformatics as it allows users to go beyond simple browsing of genome browsers, such as Ensembl [1],[2] or the UCSC Genome Browser [3], to address questions; for example, the biological meaning of the results obtained with a microarray platform, or how to identify a shor...

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Bibliographic Details
Published inPLoS computational biology Vol. 4; no. 9; p. e1000121
Main Authors Fernández-Suárez, Xosé M., Birney, Ewan
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.09.2008
Public Library of Science (PLoS)
Subjects
Bioinformatics
Carbohydrates
Computational biology
Computational Biology - methods
Computational Biology/Genomics
Data Interpretation, Statistical
Data mining
Database management systems
Databases, Factual - statistics & numerical data
Databases, Genetic - statistics & numerical data
Education
Genomes
Genomics - statistics & numerical data
Internet
Methods
Public software
Software
User-Computer Interface
United Kingdom
User-Computer Interface
Data Interpretation, Statistical
Genomics
Computational Biology
Databases, Genetic
Software
Internet
Databases, Factual
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Summary:  Data mining is vital to bioinformatics as it allows users to go beyond simple browsing of genome browsers, such as Ensembl [1],[2] or the UCSC Genome Browser [3], to address questions; for example, the biological meaning of the results obtained with a microarray platform, or how to identify a short motif upstream of a gene, amongst others. Conclusions We have seen how to go beyond simple browsing of data with data mining tools leveraging the BioMart system from different platforms, e.g., BioConductor (biomaRt), to find the association between microarray probe and Ensembl gene sets.
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ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1000121